ShinyBioHEAT: An Interactive Shiny App To Identify Phenotype Driver Genes in Ecoli and Bsubtilis

Authors

Chen Wang
Harikumar Govindarajan
Panagiotis Katsonis
Olivier Lichtarge

Publication Date

8-1-2023

Journal

Bioinformatics

DOI

10.1093/bioinformatics/btad467

PMID

37522889

PMCID

PMC10412404

PubMedCentral® Posted Date

7-31-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Escherichia coli, Mobile Applications, Software, Mutation, Data Interpretation, Statistical, Mutation Rate

Abstract

SUMMARY: In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers.

AVAILABILITY AND IMPLEMENTATION: Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.